Abstract: An anomaly analysis system generates models capable of more accurately identifying anomalies in data that contains unsatisfactory training data. The anomaly analysis system determines when data contains unsatisfactory training data. When an anomaly is detected in data using an initially selected model, and the data contains unsatisfactory training data, model reselection is performed. The reselected model analyzes the data. The reselected model is used to identify any anomalies in the data based on a data point from the data being outside of a confidence interval related to a predicted point by the reselected model corresponding to the data point.

Abstract: A server monitoring methodology uses a time-series model for predicting value of a metric of a server. The model is built using initial training data that includes median values of the metric, each median value based on previously measured values of that metric, from servers of a group to which the server is being added. The methodology includes observing the value of the metric of the server, and comparing that observed value to a predicted value of the model. In response to the observed value being within an expected tolerance, the training data is updated to include the observed value; and in response to the observed value being outside the expected tolerance, the training data is updated to include a value between the observed value of the server metric and the predicted value. The model is updated using the updated training data, and eventually adapts to performance of the server.

Abstract: A server monitoring methodology uses a time-series model for predicting value of a metric of a server. The model is built using initial training data that includes median values of the metric, each median value based on previously measured values of that metric, from servers of a group to which the server is being added. The methodology includes observing the value of the metric of the server, and comparing that observed value to a predicted value of the model. In response to the observed value being within an expected tolerance, the training data is updated to include the observed value; and in response to the observed value being outside the expected tolerance, the training data is updated to include a value between the observed value of the server metric and the predicted value. The model is updated using the updated training data, and eventually adapts to performance of the server.

Abstract: An anomaly analysis system generates models capable of more accurately identifying anomalies in data that contains unsatisfactory training data. The anomaly analysis system determines when data contains unsatisfactory training data. When an anomaly is detected in data using an initially selected model, and the data contains unsatisfactory training data, model reselection is performed. The reselected model analyzes the data. The reselected model is used to identify any anomalies in the data based on a data point from the data being outside of a confidence interval related to a predicted point by the reselected model corresponding to the data point.

Abstract: An anomaly analysis system generates models capable of more accurately identifying anomalies in data that contains unsatisfactory training data. The anomaly analysis system determines when data contains unsatisfactory training data. When an anomaly is detected in data using an initially selected model, and the data contains unsatisfactory training data, model reselection is performed. The reselected model analyzes the data. The reselected model is used to identify any anomalies in the data based on a data point from the data being outside of a confidence interval related to a predicted point by the reselected model corresponding to the data point.

Abstract: An anomaly analysis system generates models capable of more accurately identifying anomalies in data that contains unsatisfactory training data. The anomaly analysis system determines when data contains unsatisfactory training data. When an anomaly is detected in data using an initially selected model, and the data contains unsatisfactory training data, model reselection is performed. The reselected model analyzes the data. The reselected model is used to identify any anomalies in the data based on a data point from the data being outside of a confidence interval related to a predicted point by the reselected model corresponding to the data point.